Trial reportJournal of neuroengineering and rehabilitation2015
Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions.
Trial report in Journal of neuroengineering and rehabilitation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Use and evaluation of assistive technologies for upper limb function in tetraplegia.The journal of spinal cord medicine · 2022Pooled it
- Combining transcranial direct-current stimulation with gait training in patients with neurological disorders: a systematic review.Journal of neuroengineering and rehabilitation · 2019Pooled it
- EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review.Sensors (Basel, Switzerland) · 2018Pooled it
- A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives.PloS one · 2017Pooled it
- Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges.Frontiers in human neuroscience · 2025Review
- EEG-based sensorimotor neurofeedback for motor neurorehabilitation in children and adults: A scoping review.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2024Article
- Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data.Scientific reports · 2024Article
- EEG and EMG dataset for the detection of errors introduced by an active orthosis device.Frontiers in human neuroscience · 2024Article
- A Review of Brain Activity and EEG-Based Brain-Computer Interfaces for Rehabilitation Application.Bioengineering (Basel, Switzerland) · 2022Review
- Noninvasively recorded high-gamma signals improve synchrony of force feedback in a novel neurorehabilitation brain-machine interface for brain injury.Journal of neural engineering · 2022Article
- An EEG-based asynchronous MI-BCI system to reduce false positives with a small number of channels for neurorehabilitation: A pilot study.Frontiers in neurorobotics · 2022Article
- Novel Human-Centered Robotics: Towards an Automated Process for Neurorehabilitation.Neurology research international · 2021Review
- Improving Real-Time Lower Limb Motor Imagery Detection Using tDCS and an Exoskeleton.Frontiers in neuroscience · 2018Article
- Event-related desynchronization during movement attempt and execution in severely paralyzed stroke patients: An artifact removal relevance analysis.NeuroImage. Clinical · 2018Article
- Topographical measures of functional connectivity as biomarkers for post-stroke motor recovery.Journal of neuroengineering and rehabilitation · 2017Article
- Reducing the Impact of Shoulder Abduction Loading on the Classification of Hand Opening and Grasping in Individuals with Poststroke Flexion Synergy.Frontiers in bioengineering and biotechnology · 2017Article
- Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton.Frontiers in neuroscience · 2016Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAs a consequence of the increase of cerebro-vascular accidents, the number of people suffering from motor disabilities is raising. Exoskeletons, Functional Electrical Stimulation (FES) devices and Brain-Machine Interfaces (BMIs) could be combined for rehabilitation purposes in order to improve therapy outcomes.
methodsIn this work, a system based on a hybrid upper limb exoskeleton is used for neurological rehabilitation. Reaching movements are supported by the passive exoskeleton ArmeoSpring and FES. The movement execution is triggered by an EEG-based BMI. The BMI uses two different methods to interact with the exoskeleton from the user's brain activity. The first method relies on motor imagery tasks classification, whilst the second one is based on movement intention detection.
resultsThree healthy users and five patients with neurological conditions participated in the experiments to verify the usability of the system. Using the BMI based on motor imagery, healthy volunteers obtained an average accuracy of 82.9 ± 14.5 %, and patients obtained an accuracy of 65.3 ± 9.0 %, with a low False Positives rate (FP) (19.2 ± 10.4 % and 15.0 ± 8.4 %, respectively). On the other hand, by using the BMI based on detecting the arm movement intention, the average accuracy was 76.7 ± 13.2 % for healthy users and 71.6 ± 15.8 % for patients, with 28.7 ± 19.9 % and 21.2 ± 13.3 % of FP rate (healthy users and patients, respectively).
conclusionsThe accuracy of the results shows that the combined use of a hybrid upper limb exoskeleton and a BMI could be used for rehabilitation therapies. The advantage of this system is that the user is an active part of the rehabilitation procedure. The next step will be to verify what are the clinical benefits for the patients using this new rehabilitation procedure.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.